Agentic AI systems are transforming AI from passive responders into proactive agents capable of perceiving, reasoning, and autonomously executing complex, multi-step tasks. These systems are characterized by their capacity for agency, enabling them to make decisions, plan, and learn iteratively, unlike traditional reactive models. The agentic design pattern comprises four essential components: Reflection, Tool-Use, Planning, and Multi-Agent Frameworks, which together form the operational backbone for autonomous and adaptable AI. Practical applications span various sectors, including DevOps, marketing, enterprise operations, customer experience, and sales, showcasing their potential to handle high-complexity tasks and improve productivity. Technological advances like large language models, open-source and multimodal models, structured outputs, and fast inference infrastructure have enabled the development of these systems. As agentic AI becomes increasingly integrated into real-world applications, it represents a fundamental shift in software design, transitioning from static workflows to intelligent process orchestration, with AI acting as a partner rather than merely a tool.